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Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times

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  • Zhou, Shengchao
  • Jin, Mingzhou
  • Du, Ni

Abstract

Research on manufacturing scheduling has historically emphasized production efficiency. With rising environmental consciousness, manufacturing companies are paying increasing attention to energy efficiency on the shop floor. Manufacturing consumes a large amount of electricity globally. The mismatch between electric supply and demand has been a huge problem, which even becomes worse when renewable energy becomes more popular. The time-of-use (TOU) pricing policy is a widely used demand response (DR) approach, trying to align demand to supply. This paper considers energy-efficient scheduling of a single batch processing machine with non-identical job sizes and release times under a TOU electric tariff so as to simultaneously minimize total electricity cost, a criterion of environmental and energy sustainability, and makespan, a criterion of productivity. A mathematical formulation is developed to optimize electricity cost and makespan. Due to computational complexity, a hybrid multi-objective meta-heuristic algorithm is developed to find the Pareto front. Two constructive heuristics are presented to group jobs into batches. Two different approaches are presented to improve total electricity costs. The performance of the proposed model and algorithms is evaluated through extensive numerical experiments. Production managers can use the model and algorithms provided in this work to make a trade-off between productivity and sustainability.

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  • Zhou, Shengchao & Jin, Mingzhou & Du, Ni, 2020. "Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times," Energy, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:energy:v:209:y:2020:i:c:s0360544220315279
    DOI: 10.1016/j.energy.2020.118420
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    3. Xiangxin An & Guojin Si & Tangbin Xia & Qinming Liu & Yaping Li & Rui Miao, 2022. "Operation and Maintenance Optimization for Manufacturing Systems with Energy Management," Energies, MDPI, vol. 15(19), pages 1-19, October.
    4. He, Xinxin & Wang, Zhijian & Li, Yanfeng & Khazhina, Svetlana & Du, Wenhua & Wang, Junyuan & Wang, Wenzhao, 2022. "Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    6. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2021. "Job Scheduling under Time-of-Use Energy Tariffs for Sustainable Manufacturing: A Survey," LIDAM Discussion Papers CORE 2021019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.

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